/*
* J48.java
* Created on  2014-8-7 下午1:40
* 版本       修改时间          作者      修改内容
* V1.0.1    2014-8-7       panzhuowen    初始版本
*
*/
package com.genghis.ptas.j48;

/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    J48.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

import weka.classifiers.Classifier;
import weka.classifiers.Sourcable;
import weka.classifiers.trees.j48.*;
import weka.core.*;

import java.util.Enumeration;
import java.util.Vector;

/**
 * <!-- globalinfo-start -->
 * Class for generating a pruned or unpruned C4.5 decision tree. For more information, see<br/>
 * <br/>
 * Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
 * <p/>
 * <!-- globalinfo-end -->
 * <p/>
 * <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;book{Quinlan1993,
 *    address = {San Mateo, CA},
 *    author = {Ross Quinlan},
 *    publisher = {Morgan Kaufmann Publishers},
 *    title = {C4.5: Programs for Machine Learning},
 *    year = {1993}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * <p/>
 * <!-- options-start -->
 * Valid options are: <p/>
 * <p/>
 * <pre> -U
 *  Use unpruned tree.</pre>
 * <p/>
 * <pre> -C &lt;pruning confidence&gt;
 *  Set confidence threshold for pruning.
 *  (default 0.25)</pre>
 * <p/>
 * <pre> -M &lt;minimum number of instances&gt;
 *  Set minimum number of instances per leaf.
 *  (default 2)</pre>
 * <p/>
 * <pre> -R
 *  Use reduced error pruning.</pre>
 * <p/>
 * <pre> -N &lt;number of folds&gt;
 *  Set number of folds for reduced error
 *  pruning. One fold is used as pruning set.
 *  (default 3)</pre>
 * <p/>
 * <pre> -B
 *  Use binary splits only.</pre>
 * <p/>
 * <pre> -S
 *  Don't perform subtree raising.</pre>
 * <p/>
 * <pre> -L
 *  Do not clean up after the tree has been built.</pre>
 * <p/>
 * <pre> -A
 *  Laplace smoothing for predicted probabilities.</pre>
 * <p/>
 * <pre> -Q &lt;seed&gt;
 *  Seed for random data shuffling (default 1).</pre>
 * <p/>
 * <!-- options-end -->
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision: 1.9 $
 */
public class J48
        extends Classifier
        implements OptionHandler, Drawable, Matchable, Sourcable,
        WeightedInstancesHandler, Summarizable, AdditionalMeasureProducer,
        TechnicalInformationHandler {

    /**
     * for serialization
     */
    static final long serialVersionUID = -217733168393644444L;

    /**
     * The decision tree
     */
    private ClassifierTree m_root;

    /**
     * Unpruned tree?
     */
    private boolean m_unpruned = false;

    /**
     * Confidence level
     */
    private float m_CF = 0.25f;

    /**
     * Minimum number of instances
     */
    private int m_minNumObj = 2;

    /**
     * Determines whether probabilities are smoothed using
     * Laplace correction when predictions are generated
     */
    private boolean m_useLaplace = false;

    /**
     * Use reduced error pruning?
     */
    private boolean m_reducedErrorPruning = false;

    /**
     * Number of folds for reduced error pruning.
     */
    private int m_numFolds = 3;

    /**
     * Binary splits on nominal attributes?
     */
    private boolean m_binarySplits = false;

    /**
     * Subtree raising to be performed?
     */
    private boolean m_subtreeRaising = true;

    /**
     * Cleanup after the tree has been built.
     */
    private boolean m_noCleanup = false;

    /**
     * Random number seed for reduced-error pruning.
     */
    private int m_Seed = 1;

    /**
     * Returns a string describing classifier
     *
     * @return a description suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String globalInfo() {

        return "Class for generating a pruned or unpruned C4.5 decision tree. For more "
                + "information, see\n\n"
                + getTechnicalInformation().toString();
    }

    /**
     * Returns an instance of a TechnicalInformation object, containing
     * detailed information about the technical background of this class,
     * e.g., paper reference or book this class is based on.
     *
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;

        result = new TechnicalInformation(TechnicalInformation.Type.BOOK);
        result.setValue(TechnicalInformation.Field.AUTHOR, "Ross Quinlan");
        result.setValue(TechnicalInformation.Field.YEAR, "1993");
        result.setValue(TechnicalInformation.Field.TITLE, "C4.5: Programs for Machine Learning");
        result.setValue(TechnicalInformation.Field.PUBLISHER, "Morgan Kaufmann Publishers");
        result.setValue(TechnicalInformation.Field.ADDRESS, "San Mateo, CA");

        return result;
    }

    /**
     * Returns default capabilities of the classifier.
     *
     * @return the capabilities of this classifier
     */
    public Capabilities getCapabilities() {
        Capabilities result;

        try {
            if (!m_reducedErrorPruning)
                result = new C45PruneableClassifierTree(null, !m_unpruned, m_CF, m_subtreeRaising, !m_noCleanup).getCapabilities();
            else
                result = new PruneableClassifierTree(null, !m_unpruned, m_numFolds, !m_noCleanup, m_Seed).getCapabilities();
        } catch (Exception e) {
            result = new Capabilities(this);
        }

        result.setOwner(this);

        return result;
    }

    /**
     * Generates the classifier.
     *
     * @param instances the data to train the classifier with
     * @throws Exception if classifier can't be built successfully
     */
    public void buildClassifier(Instances instances)
            throws Exception {

        ModelSelection modSelection;

        if (m_binarySplits)
            modSelection = new BinC45ModelSelection(m_minNumObj, instances);
        else
            modSelection = new C45ModelSelection(m_minNumObj, instances);
        if (!m_reducedErrorPruning)
            m_root = new C45PruneableClassifierTree(modSelection, !m_unpruned, m_CF,
                    m_subtreeRaising, !m_noCleanup);
        else
            m_root = new PruneableClassifierTree(modSelection, !m_unpruned, m_numFolds,
                    !m_noCleanup, m_Seed);
        m_root.buildClassifier(instances);
        if (m_binarySplits) {
            ((BinC45ModelSelection) modSelection).cleanup();
        } else {
            ((C45ModelSelection) modSelection).cleanup();
        }
    }

    /**
     * Classifies an instance.
     *
     * @param instance the instance to classify
     * @return the classification for the instance
     * @throws Exception if instance can't be classified successfully
     */
    public double classifyInstance(Instance instance) throws Exception {

        return m_root.classifyInstance(instance);
    }

    /**
     * Returns class probabilities for an instance.
     *
     * @param instance the instance to calculate the class probabilities for
     * @return the class probabilities
     * @throws Exception if distribution can't be computed successfully
     */
    public final double[] distributionForInstance(Instance instance)
            throws Exception {

        return m_root.distributionForInstance(instance, m_useLaplace);
    }

    /**
     * Returns the type of graph this classifier
     * represents.
     *
     * @return Drawable.TREE
     */
    public int graphType() {
        return Drawable.TREE;
    }

    /**
     * Returns graph describing the tree.
     *
     * @return the graph describing the tree
     * @throws Exception if graph can't be computed
     */
    public String graph() throws Exception {

        return m_root.graph();
    }

    /**
     * Returns tree in prefix order.
     *
     * @return the tree in prefix order
     * @throws Exception if something goes wrong
     */
    public String prefix() throws Exception {

        return m_root.prefix();
    }


    /**
     * Returns tree as an if-then statement.
     *
     * @param className the name of the Java class
     * @return the tree as a Java if-then type statement
     * @throws Exception if something goes wrong
     */
    public String toSource(String className) throws Exception {

        StringBuffer[] source = m_root.toSource(className);
        return
                "class " + className + " {\n\n"
                        + "  public static double classify(Object[] i)\n"
                        + "    throws Exception {\n\n"
                        + "    double p = Double.NaN;\n"
                        + source[0]  // Assignment code
                        + "    return p;\n"
                        + "  }\n"
                        + source[1]  // Support code
                        + "}\n";
    }

    /**
     * Returns an enumeration describing the available options.
     * <p/>
     * Valid options are: <p>
     * <p/>
     * -U <br>
     * Use unpruned tree.<p>
     * <p/>
     * -C confidence <br>
     * Set confidence threshold for pruning. (Default: 0.25) <p>
     * <p/>
     * -M number <br>
     * Set minimum number of instances per leaf. (Default: 2) <p>
     * <p/>
     * -R <br>
     * Use reduced error pruning. No subtree raising is performed. <p>
     * <p/>
     * -N number <br>
     * Set number of folds for reduced error pruning. One fold is
     * used as the pruning set. (Default: 3) <p>
     * <p/>
     * -B <br>
     * Use binary splits for nominal attributes. <p>
     * <p/>
     * -S <br>
     * Don't perform subtree raising. <p>
     * <p/>
     * -L <br>
     * Do not clean up after the tree has been built.
     * <p/>
     * -A <br>
     * If set, Laplace smoothing is used for predicted probabilites. <p>
     * <p/>
     * -Q <br>
     * The seed for reduced-error pruning. <p>
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {

        Vector newVector = new Vector(9);

        newVector.
                addElement(new Option("\tUse unpruned tree.",
                        "U", 0, "-U"));
        newVector.
                addElement(new Option("\tSet confidence threshold for pruning.\n" +
                        "\t(default 0.25)",
                        "C", 1, "-C <pruning confidence>"));
        newVector.
                addElement(new Option("\tSet minimum number of instances per leaf.\n" +
                        "\t(default 2)",
                        "M", 1, "-M <minimum number of instances>"));
        newVector.
                addElement(new Option("\tUse reduced error pruning.",
                        "R", 0, "-R"));
        newVector.
                addElement(new Option("\tSet number of folds for reduced error\n" +
                        "\tpruning. One fold is used as pruning set.\n" +
                        "\t(default 3)",
                        "N", 1, "-N <number of folds>"));
        newVector.
                addElement(new Option("\tUse binary splits only.",
                        "B", 0, "-B"));
        newVector.
                addElement(new Option("\tDon't perform subtree raising.",
                        "S", 0, "-S"));
        newVector.
                addElement(new Option("\tDo not clean up after the tree has been built.",
                        "L", 0, "-L"));
        newVector.
                addElement(new Option("\tLaplace smoothing for predicted probabilities.",
                        "A", 0, "-A"));
        newVector.
                addElement(new Option("\tSeed for random data shuffling (default 1).",
                        "Q", 1, "-Q <seed>"));

        return newVector.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * <!-- options-start -->
     * Valid options are: <p/>
     * <p/>
     * <pre> -U
     *  Use unpruned tree.</pre>
     * <p/>
     * <pre> -C &lt;pruning confidence&gt;
     *  Set confidence threshold for pruning.
     *  (default 0.25)</pre>
     * <p/>
     * <pre> -M &lt;minimum number of instances&gt;
     *  Set minimum number of instances per leaf.
     *  (default 2)</pre>
     * <p/>
     * <pre> -R
     *  Use reduced error pruning.</pre>
     * <p/>
     * <pre> -N &lt;number of folds&gt;
     *  Set number of folds for reduced error
     *  pruning. One fold is used as pruning set.
     *  (default 3)</pre>
     * <p/>
     * <pre> -B
     *  Use binary splits only.</pre>
     * <p/>
     * <pre> -S
     *  Don't perform subtree raising.</pre>
     * <p/>
     * <pre> -L
     *  Do not clean up after the tree has been built.</pre>
     * <p/>
     * <pre> -A
     *  Laplace smoothing for predicted probabilities.</pre>
     * <p/>
     * <pre> -Q &lt;seed&gt;
     *  Seed for random data shuffling (default 1).</pre>
     * <p/>
     * <!-- options-end -->
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {

        // Other options
        String minNumString = Utils.getOption('M', options);
        if (minNumString.length() != 0) {
            m_minNumObj = Integer.parseInt(minNumString);
        } else {
            m_minNumObj = 2;
        }
        m_binarySplits = Utils.getFlag('B', options);
        m_useLaplace = Utils.getFlag('A', options);

        // Pruning options
        m_unpruned = Utils.getFlag('U', options);
        m_subtreeRaising = !Utils.getFlag('S', options);
        m_noCleanup = Utils.getFlag('L', options);
        if ((m_unpruned) && (!m_subtreeRaising)) {
            throw new Exception("Subtree raising doesn't need to be unset for unpruned tree!");
        }
        m_reducedErrorPruning = Utils.getFlag('R', options);
        if ((m_unpruned) && (m_reducedErrorPruning)) {
            throw new Exception("Unpruned tree and reduced error pruning can't be selected " +
                    "simultaneously!");
        }
        String confidenceString = Utils.getOption('C', options);
        if (confidenceString.length() != 0) {
            if (m_reducedErrorPruning) {
                throw new Exception("Setting the confidence doesn't make sense " +
                        "for reduced error pruning.");
            } else if (m_unpruned) {
                throw new Exception("Doesn't make sense to change confidence for unpruned "
                        + "tree!");
            } else {
                m_CF = (new Float(confidenceString)).floatValue();
                if ((m_CF <= 0) || (m_CF >= 1)) {
                    throw new Exception("Confidence has to be greater than zero and smaller " +
                            "than one!");
                }
            }
        } else {
            m_CF = 0.25f;
        }
        String numFoldsString = Utils.getOption('N', options);
        if (numFoldsString.length() != 0) {
            if (!m_reducedErrorPruning) {
                throw new Exception("Setting the number of folds" +
                        " doesn't make sense if" +
                        " reduced error pruning is not selected.");
            } else {
                m_numFolds = Integer.parseInt(numFoldsString);
            }
        } else {
            m_numFolds = 3;
        }
        String seedString = Utils.getOption('Q', options);
        if (seedString.length() != 0) {
            m_Seed = Integer.parseInt(seedString);
        } else {
            m_Seed = 1;
        }
    }

    /**
     * Gets the current settings of the Classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {

        String[] options = new String[14];
        int current = 0;

        if (m_noCleanup) {
            options[current++] = "-L";
        }
        if (m_unpruned) {
            options[current++] = "-U";
        } else {
            if (!m_subtreeRaising) {
                options[current++] = "-S";
            }
            if (m_reducedErrorPruning) {
                options[current++] = "-R";
                options[current++] = "-N";
                options[current++] = "" + m_numFolds;
                options[current++] = "-Q";
                options[current++] = "" + m_Seed;
            } else {
                options[current++] = "-C";
                options[current++] = "" + m_CF;
            }
        }
        if (m_binarySplits) {
            options[current++] = "-B";
        }
        options[current++] = "-M";
        options[current++] = "" + m_minNumObj;
        if (m_useLaplace) {
            options[current++] = "-A";
        }

        while (current < options.length) {
            options[current++] = "";
        }
        return options;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String seedTipText() {
        return "The seed used for randomizing the data " +
                "when reduced-error pruning is used.";
    }

    /**
     * Get the value of Seed.
     *
     * @return Value of Seed.
     */
    public int getSeed() {

        return m_Seed;
    }

    /**
     * Set the value of Seed.
     *
     * @param newSeed Value to assign to Seed.
     */
    public void setSeed(int newSeed) {

        m_Seed = newSeed;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String useLaplaceTipText() {
        return "Whether counts at leaves are smoothed based on Laplace.";
    }

    /**
     * Get the value of useLaplace.
     *
     * @return Value of useLaplace.
     */
    public boolean getUseLaplace() {

        return m_useLaplace;
    }

    /**
     * Set the value of useLaplace.
     *
     * @param newuseLaplace Value to assign to useLaplace.
     */
    public void setUseLaplace(boolean newuseLaplace) {

        m_useLaplace = newuseLaplace;
    }

    /**
     * Returns a description of the classifier.
     *
     * @return a description of the classifier
     */
    public String toString() {

        if (m_root == null) {
            return "No classifier built";
        }
        if (m_unpruned)
            return "J48 unpruned tree\n------------------\n" + m_root.toString();
        else
            return "J48 pruned tree\n------------------\n" + m_root.toString();
    }

    /**
     * Returns a superconcise version of the model
     *
     * @return a summary of the model
     */
    public String toSummaryString() {

        return "Number of leaves: " + m_root.numLeaves() + "\n"
                + "Size of the tree: " + m_root.numNodes() + "\n";
    }

    /**
     * Returns the size of the tree
     *
     * @return the size of the tree
     */
    public double measureTreeSize() {
        return m_root.numNodes();
    }

    /**
     * Returns the number of leaves
     *
     * @return the number of leaves
     */
    public double measureNumLeaves() {
        return m_root.numLeaves();
    }

    /**
     * Returns the number of rules (same as number of leaves)
     *
     * @return the number of rules
     */
    public double measureNumRules() {
        return m_root.numLeaves();
    }

    /**
     * Returns an enumeration of the additional measure names
     *
     * @return an enumeration of the measure names
     */
    public Enumeration enumerateMeasures() {
        Vector newVector = new Vector(3);
        newVector.addElement("measureTreeSize");
        newVector.addElement("measureNumLeaves");
        newVector.addElement("measureNumRules");
        return newVector.elements();
    }

    /**
     * Returns the value of the named measure
     *
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String additionalMeasureName) {
        if (additionalMeasureName.compareToIgnoreCase("measureNumRules") == 0) {
            return measureNumRules();
        } else if (additionalMeasureName.compareToIgnoreCase("measureTreeSize") == 0) {
            return measureTreeSize();
        } else if (additionalMeasureName.compareToIgnoreCase("measureNumLeaves") == 0) {
            return measureNumLeaves();
        } else {
            throw new IllegalArgumentException(additionalMeasureName
                    + " not supported (j48)");
        }
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String unprunedTipText() {
        return "Whether pruning is performed.";
    }

    /**
     * Get the value of unpruned.
     *
     * @return Value of unpruned.
     */
    public boolean getUnpruned() {

        return m_unpruned;
    }

    /**
     * Set the value of unpruned. Turns reduced-error pruning
     * off if set.
     *
     * @param v Value to assign to unpruned.
     */
    public void setUnpruned(boolean v) {

        if (v) {
            m_reducedErrorPruning = false;
        }
        m_unpruned = v;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String confidenceFactorTipText() {
        return "The confidence factor used for pruning (smaller values incur "
                + "more pruning).";
    }

    /**
     * Get the value of CF.
     *
     * @return Value of CF.
     */
    public float getConfidenceFactor() {

        return m_CF;
    }

    /**
     * Set the value of CF.
     *
     * @param v Value to assign to CF.
     */
    public void setConfidenceFactor(float v) {

        m_CF = v;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String minNumObjTipText() {
        return "The minimum number of instances per leaf.";
    }

    /**
     * Get the value of minNumObj.
     *
     * @return Value of minNumObj.
     */
    public int getMinNumObj() {

        return m_minNumObj;
    }

    /**
     * Set the value of minNumObj.
     *
     * @param v Value to assign to minNumObj.
     */
    public void setMinNumObj(int v) {

        m_minNumObj = v;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String reducedErrorPruningTipText() {
        return "Whether reduced-error pruning is used instead of C.4.5 pruning.";
    }

    /**
     * Get the value of reducedErrorPruning.
     *
     * @return Value of reducedErrorPruning.
     */
    public boolean getReducedErrorPruning() {

        return m_reducedErrorPruning;
    }

    /**
     * Set the value of reducedErrorPruning. Turns
     * unpruned trees off if set.
     *
     * @param v Value to assign to reducedErrorPruning.
     */
    public void setReducedErrorPruning(boolean v) {

        if (v) {
            m_unpruned = false;
        }
        m_reducedErrorPruning = v;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String numFoldsTipText() {
        return "Determines the amount of data used for reduced-error pruning. "
                + " One fold is used for pruning, the rest for growing the tree.";
    }

    /**
     * Get the value of numFolds.
     *
     * @return Value of numFolds.
     */
    public int getNumFolds() {

        return m_numFolds;
    }

    /**
     * Set the value of numFolds.
     *
     * @param v Value to assign to numFolds.
     */
    public void setNumFolds(int v) {

        m_numFolds = v;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String binarySplitsTipText() {
        return "Whether to use binary splits on nominal attributes when "
                + "building the trees.";
    }

    /**
     * Get the value of binarySplits.
     *
     * @return Value of binarySplits.
     */
    public boolean getBinarySplits() {

        return m_binarySplits;
    }

    /**
     * Set the value of binarySplits.
     *
     * @param v Value to assign to binarySplits.
     */
    public void setBinarySplits(boolean v) {

        m_binarySplits = v;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String subtreeRaisingTipText() {
        return "Whether to consider the subtree raising operation when pruning.";
    }

    /**
     * Get the value of subtreeRaising.
     *
     * @return Value of subtreeRaising.
     */
    public boolean getSubtreeRaising() {

        return m_subtreeRaising;
    }

    /**
     * Set the value of subtreeRaising.
     *
     * @param v Value to assign to subtreeRaising.
     */
    public void setSubtreeRaising(boolean v) {

        m_subtreeRaising = v;
    }

    /**
     * Returns the tip text for this property
     *
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String saveInstanceDataTipText() {
        return "Whether to save the training data for visualization.";
    }

    /**
     * Check whether instance data is to be saved.
     *
     * @return true if instance data is saved
     */
    public boolean getSaveInstanceData() {

        return m_noCleanup;
    }

    /**
     * Set whether instance data is to be saved.
     *
     * @param v true if instance data is to be saved
     */
    public void setSaveInstanceData(boolean v) {

        m_noCleanup = v;
    }

    /**
     * Returns the revision string.
     *
     * @return the revision
     */
    public String getRevision() {
        return RevisionUtils.extract("$Revision: 1.9 $");
    }

    /**
     * return the result with collection
     *
     * @return the result with collection
     */



    /**
     * Main method for testing this class
     *
     * @param argv the commandline options
     */
    public static void main(String[] argv) {
        runClassifier(new J48(), argv);
    }
}

